Adaptive obstacle avoidance in path planning of collaborative robots for dynamic manufacturing

Hu, Yudie; Wang, Yuqi; Hu, Kaixiong; Li, Weidong

doi:10.1007/s10845-021-01825-9

Cited by 15 publications

(11 citation statements)

References 25 publications

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“…Several articles in the literature review showed that implementation looks easy in theory but could be tricky in practice. Such as the use of digital instructions for machine tool setup [45], au-tomated dynamic planning and scheduling of production process [31], [50], algorithms for automated line balancing [19], [51], but few articles show fully implementation of operational flexibility with Industry 4.0 digital tools and capabilities. It is important to consider the entire system when implementing an industry 4.0 enabling technology towards increasing operational flexibility [52].…”

Section: Discussionmentioning

confidence: 99%

Industry 4.0 enabling technologies for increasing operational flexibility in final assembly

Salunkhe¹,

Berglund²

2022

Int J Ind Eng Manag

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The manufacturing industry is facing uncertainties caused by growing competition and increasing customer demands. Simultaneously, the fourth industrial revolution, commonly referred to as Industry 4.0, is helping in modernising the manufacturing industry. In the process of modernising, companies are now capable of building resilience into their systems. This resilience is in the form of higher operational flexibility, which helps cope with the growing uncertainties. The new technologies under the Industry 4.0 umbrella can be used to increase operational flexibility. This article summarises various Industry 4.0 enabling technologies that can increase operational flexibility in final assembly.

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Section: Discussionmentioning

confidence: 99%

Industry 4.0 enabling technologies for increasing operational flexibility in final assembly

Salunkhe¹,

Berglund²

2022

Int J Ind Eng Manag

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“…Statistical filtering is effective in removing scattered points along the edges of planes. However, some noise points do not follow the Gaussian distribution characteristics [13], making it difficult to remove them using statistical filtering alone. Therefore, it is necessary to introduce radius filtering [14] on top of statistical filtering to remove other scattered points and improve the quality of the point cloud data.…”

Section: Point Cloud Data Preprocessingmentioning

confidence: 99%

Dimensional measurement of anti-stress cone of HV cable joints based on 3D point cloud

Zheng

Gao

et al. 2023

Meas. Sci. Technol.

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To solve the problem of automatic measurement of anti-stress cone of HV(high voltage) cable joints, a measurement algorithm based on 3D point cloud is proposed. Firstly, denoising and coordinate adjustment of point cloud of a cable joint are realized. Then the characteristic curve model of fitting circle radius indicating structure characteristic of cable joints is constructed by fitting circles with RANSAC algorithm . After that, the discrete points on the characteristic curve are extracted according to the characteristics such as the fluctuation range of the characteristic curve for straight line fitting and the intersection of the fitted lines is calculated to complete the segmentation of the anti-stress cone area of the cable joint. Finally, the parameter measurement of the anti-stress cone was completed according to the structure of the anti-stress cone, and the error correction post-processing was performed. Two types of cable joint point clouds with standard dimensions and surface defects were used for measurement experiments, the absolute errors of the measurement results are less than 0.2mm and 1.0mm respectively, and the relative errors are less than 0.25% and 2% respectively, indicating that the proposed method has high robustness and measurement accuracy,which meet the measurement requirements in engineering practice.

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“…Task-related parameters include the coordinate system at the end of the task trajectory and the markers that affect the task path (whether or not it is on the task path) (Calinon, 2015). Generally, the TP-LfD method is applied to robot obstacle avoidance (Hu et al , 2021; El Zaatari and Li, 2019; El Zaatari et al , 2021, 2019) and manipulation tasks (Sena et al , 2019; Zaatari et al , 2021a, 2021b; Realyvásquez-Vargas et al , 2019; Tamas and Murar, 2018). Assigning different weights to each task parameter is an important method to improve model generalization performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhanced task parameterized dynamic movement primitives by GMM to solve manipulation tasks

Cong

Dong

et al. 2023

RIA

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Purpose Under the development trend of intelligent manufacturing, the unstructured environment requires the robot to have a good generalization performance to adapt to the scene changes. The purpose of this paper aims to present a learning from demonstration (LfD) method (task parameterized [TP]-dynamic movement primitives [DMP]-GMR) that combines DMPs and TP-LfD to improve generalization performance and solve object manipulation tasks. Design/methodology/approach The dynamic time warping algorithm is applied to processing demonstration data to obtain a more standard learning model in the proposed method. The DMPs are used to model the basic trajectory learning model. The Gaussian mixture model is introduced to learn the force term of DMPs and solve the problem of learning from multiple demonstration trajectories. The robot can learn more local geometric features and generalize the learned model to unknown situations by adding task parameters. Findings An evaluation criterion based on curve similarity calculated by the Frechet distance was constructed to evaluate the model’s interpolation and extrapolation performance. The model’s generalization performance was assessed on 2D virtual data sets, and first, the results show that the proposed method has better interpolation and extrapolation performance than other methods. Originality/value The proposed model was applied to the axle-hole assembly task on real robots, and the robot’s posture in grasping and placing the axle part was taken as the task parameter of the model. The experiment results show that The proposed model is competitive with other models.

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Adaptive obstacle avoidance in path planning of collaborative robots for dynamic manufacturing

Cited by 15 publications

References 25 publications

Industry 4.0 enabling technologies for increasing operational flexibility in final assembly

Industry 4.0 enabling technologies for increasing operational flexibility in final assembly

Dimensional measurement of anti-stress cone of HV cable joints based on 3D point cloud

Enhanced task parameterized dynamic movement primitives by GMM to solve manipulation tasks

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